Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add geezerrrr/motive --skill model-usagegit clone --depth 1 https://github.com/geezerrrr/motiveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/geezerrrr/motive/model-usage)<a href="https://agentmods.dev/skills/geezerrrr/motive/model-usage"><img src="https://agentmods.dev/badge/skills/geezerrrr/motive/model-usage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/geezerrrr/motive/model-usage"><img src="https://agentmods.dev/badge/skills/geezerrrr/motive/model-usage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.00563 |
| Opus 5.5 | $0.00028 | $0.00225 |
| Sonnet 5.5 | $0.00014 | $0.00113 |
| Haiku 4.5 | $0.00007 | $0.00056 |
Grade A, and why
model-usage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to model-usage — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Model usage
Overview
Get per-model usage cost from CodexBar's local cost logs. Supports "current model" (most recent daily entry) or "all models" summaries for Codex or Claude.
TODO: add Linux CLI support guidance once CodexBar CLI install path is documented for Linux.
Quick start
- Fetch cost JSON via CodexBar CLI or pass a JSON file.
- Use the bundled script to summarize by model.
python {baseDir}/scripts/model_usage.py --provider codex --mode current
python {baseDir}/scripts/model_usage.py --provider codex --mode all
python {baseDir}/scripts/model_usage.py --provider claude --mode all --format json --pretty
Current model logic
- Uses the most recent daily row with
modelBreakdowns. - Picks the model with the highest cost in that row.
- Falls back to the last entry in
modelsUsedwhen breakdowns are missing. - Override with
--model <name>when you need a specific model.
Inputs
- Default: runs
codexbar cost --format json --provider <codex|claude>. - File or stdin:
codexbar cost --provider codex --format json > /tmp/cost.json
python {baseDir}/scripts/model_usage.py --input /tmp/cost.json --mode all
cat /tmp/cost.json | python {baseDir}/scripts/model_usage.py --input - --mode current
Output
- Text (default) or JSON (
--format json --pretty). - Values are cost-only per model; tokens are not split by model in CodexBar output.
References
- Read
references/codexbar-cli.mdfor CLI flags and cost JSON fields.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 70 lines · 69 tokens per session scan A b88045ef1c5c
model-usage is a skill published in the GitHub repository geezerrrr/motive (118 stars, last pushed 10d ago), licensed MIT. It adds 69 tokens to every session and 563 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to model-usage, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
issue-maintenance
Writes to a repo's open GitHub issues: fixes type and lifecycle labels, re-scores priority, fills sizing labels, cleans up issue prose, and proposes merging clusters of small issues. Auto-applies safe changes, flags the rest. Trigger on: "run issue maintenance", "tidy the backlog", "re-prioritize open issues"…
test-reaper
Finds and removes dead or trivial tests: duplicates, mock-only asserts, render checks that prove nothing. Also finds over-mocked tests and seam gaps, where no test sends a realistic payload across a boundary. Trigger on: "reap tests", "our tests don't test anything", "too much mocking", "why didn't tests catch this".
evidence-check
Honesty gate that backs claims with real evidence instead of stating guesses as fact. Runs always-on as a self-check before technical claims and recommendations. Invoke explicitly on: "show your work", "prove it", "how do you know that", "what's your source".
test-workflow
Automatically run after editing source files, before committing, to pick and run the right suite: unit, E2E, visual regression, or CSS lint. Do NOT wait for the user to ask. Protects running dev servers. Trigger on: src/ edits before a commit, "run tests", "check tests", "make sure tests pass".
comment-reaper
Finds and removes comments that add nothing: ones that restate the code, cite issue numbers, narrate refactor history, or pad a one-liner with JSDoc. Keeps the WHY comments. Trigger on: "reap comments", "remove unnecessary comments", "too many comments", "comment audit".
plain-language-audit
Sweeps comments, docstrings, docs, and UI copy for jargon, AI writing tells, and wordy text. Auto-fixes clear swaps and surfaces judgment calls. Prose and tone only. Trigger on: "plain language audit", "find jargon", "sounds like AI wrote this", "too wordy".